This paper is organized around two main endeavours. On the one hand, we examine currently available Computer Assisted Pronunciation Training (CAPT) systems with a view to establishing whether they meet pedagogically sound requirements. In this respect, we show that many commercial systems tend to prefer technological novelties to the detriment of pedagogical criteria that could benefit the learner more. On the other hand, we more narrowly focus on the crucial issue of computer-generated feedback, which still represents a big challenge for state-of-the-art CAPT technology and discuss its impact on learning. In the final part of the paper, we present the PROO project (Programma voor Onderwijsonderzoek), which is aimed at establishing the effects of erroneous feedback on the acquisition of L2 pronunciation.

The EduSpeak system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. We first report results on the application of adaptation techniques to recognize both native and nonnative speech in a speaker-independent manner. We discuss our pronunciation scoring paradigm and show experimental results in the form of correlations between the pronunciation quality estimators included in the toolkit and grades given by human listeners. We review phone-level pronunciation estimation schemes and describe the phone-level mispronunciation detection functionality that we have incorporated in the toolkit. Finally, we mention some of the EduSpeak toolkit system features that facilitate the creation and deployment of computer-assisted language learning (CALL) applications.

Predicting the degree of nativeness of a student utterance is an important issue in computer-aided language learning. This task has been addressed by many studies focusing on the segmental assessment of the speech signal. To achieve improved correlations between human and automatic nativeness scores, other aspects of speech should also be considered, such as prosody. The goal of this study is to evaluate the use of prosodic information to help predict the degree of nativeness of pronunciation, independent of the text. A supervised strategy based on human grades is used in an attempt to select promising features for this task. Preliminary results show improvements in the corre- lation between human and automatic scores.

Giving feedback on the degree of nativeness of a student's speech is an important aspect of computer-aided language learning. This task has been addressed by many studies focusing on the segmental assessment of the speech signal. To better model human nativeness scores, other aspects of speech should also be considered, such as prosody. This study examines the use of prosodic information to evaluate the degree of nativeness of student pronunciation, independent of the text. Supervised strategies based on human grades are used in an attempt to select promising features for this task. Previous results obtained with non-native speakers showed improvements in the correlation between human and automatic scores. New strategies were evaluated with tests including native and non-native speakers. Specific features based on durations, namely for intra-sentence pauses, revealed potential use for further improvements.

In this paper, we examine various studies and reviews on the usability of Automatic Speech Recognition (ASR) technology as a tool to train pronunciation in the second language (L2). We show that part of the criticism that has been addressed to this technology is not warranted, being rather the result of limited familiarity with ASR technology and with broader Computer Assisted Language Learning (CALL) courseware design matters. In our analysis we also consider actual problems of state-of-the-art ASR technology, with a view to indicating how ASR can be employed to develop courseware that is both pedagogically sound and reliable.

Our proposed paradigm for automatic assessment of pronunciation quality uses hidden Markov models (HMMs) to generate phonetic segmentations of the student’s speech. From these segmentations, we use the HMMs to obtain spectral match and duration scores. In this work we focus on the problem of calibrating different machine scores to obtain an accurate prediction of the grades that a human expert would assign to the pronunciation. We discuss the application of different approaches based on minimum mean square error (MMSE) estimation and Bayesian classification. We investigate the characteristics of the different mappings as well as the effects of the prior distribution of grades in the calibration database. We finally suggest a simple method to extrapolate mappings from one language to another. 1.

In this paper, we examine the relationship between pedagogy and technology in Computer Assisted Pronunciation Training (CAPT) courseware. First, we will analyse available literature on second language pronunciation teaching and learning in order to derive some general guidelines for effective training. Second, we will present an appraisal of various CAPT systems with a view to establishing whether they meet pedagogical requirements. In this respect, we will show that many commercial systems tend to prefer technological novelties to the detriment of pedagogical criteria that could benefit the learner more. While examining the limitations of today's technology, we will consider possible ways to deal with these shortcomings. Finally, we will combine the information thus gathered to suggest some recommendations for future CAPT.

Pronunciation variability is present in both native and foreign words. Since pronunciation variability constitutes a problem for automatic speech recognition (ASR) systems, modeling pronunciation variation for ASR has been the topic of various studies. In most studies, modeling pronunciation variation was attempted within the standard framework used in mainstream ASR systems. Given that some assumptions made within this framework are not in line with the properties of speech signals and the findings in human speech recognition, and that the improvements obtained by modeling pronunciation variation within this framework have generally been small, it might be better to look for a new paradigm in which pronunciation variation can be modeled more accurately. In this paper a novel paradigm for ASR is presented, which has many potential advantages for modeling pronunciation variation.

www.speech.sri.com, www.EduSpeak.com The EduSpeak TM system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. We first report results on the application of adaptation techniques to recognize both native and nonnative speech in a speaker-independent manner. We discuss our pronunciation scoring paradigm and show experimental results in the form of correlations between the pronunciation quality estimators included in the toolkit and grades given by human listeners. We review phone-level pronunciation estimation schemes and describe the phone-level mispronunciation detection functionality that we have incorporated in the toolkit. Finally, we mention some of the EduSpeak TM toolkit system features that facilitate the creation and deployment of computer-assisted language learning (CALL) applications. 1.

Commercial products that support L2-learners with computer assisted pronunciation training usually focus per exercise only on one possible pronunciation mistake that is typical for speakers of the respective L1 group. Acoustic models for words with wrong pronunciation are added to the system. In the present paper a more general approach with features that have proved to be widely independent of the learners ’ mother tongue is proposed. It is able to take various possible mistakes into consideration all at once. High dimensional feature vectors that encode prosodic varieties and differences of reference and recognized sentences are analyzed. With the ADABOOST algorithm those features are found, which contain the most important information to assess German children learning English. With 35 features 89 % of the agreement of experts is achieved. Index Terms — feature extraction, speech intelligibility 1.